library(MASS)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.7 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ dplyr::select() masks MASS::select()
library(ggplot2)
library(readr)
library(viridis)
Loading required package: viridisLite
library(ggfortify)
library(bbmle) #For ICtab
Loading required package: stats4
Attaching package: ‘bbmle’
The following object is masked from ‘package:dplyr’:
slice
library(car)
Loading required package: carData
Attaching package: ‘car’
The following object is masked from ‘package:dplyr’:
recode
The following object is masked from ‘package:purrr’:
some
library(emmeans)
round_any <- function(x, accuracy, f=round){f(x/ accuracy) * accuracy}
round_any <- function(x, accuracy, f=round){f(x/ accuracy) * accuracy}
# trial_types <- c("none","brood","worker","queen","all")
#
# for (t in trial_types){
# bee_param_df <- list.files(path=paste0("hive_data/heat_",t,"/"), full.names = TRUE) %>%
# lapply(read_csv, show_col_types = FALSE) %>%
# bind_rows %>%
# mutate(filenames = list.files(path=paste0("hive_data/heat_",t,"/"), full.names = TRUE))
#
# write.csv(bee_param_df,paste0("bee_data_isaac_heat_",t,".csv"),row.names = FALSE)
# }
What these all mean: n = queen cells per hour rb = brood radius rn =
necter radius w = total daily honey pph = pollen ratio ph = honey
consumption ratio pp = pollen consumption ratio k = consumption
probability value
broodMetric = average number of brood surrounding other brood
pollenRing = average min distance between honey and brood
brood_bee_param <- read.csv("bee_data_isaac_heat_brood.csv") %>% mutate(type = "Brood") %>% mutate(wha = 0, qha = 0)
worker_bee_param <- read.csv("bee_data_isaac_heat_worker.csv") %>% mutate(type = "Worker") %>% mutate(bhd = 0, qha = 0)
queen_bee_param <- read.csv("bee_data_isaac_heat_queen.csv") %>% mutate(type = "Queen") %>% mutate(bhd = 0, wha = 0)
none_bee_param <- read.csv("bee_data_isaac_heat_none.csv") %>% mutate(type = "None") %>% mutate(bhd = 0,wha = 0, qha = 0)
all_bee_param <- read.csv("bee_data_isaac_heat_all.csv") %>% mutate(type = "All")
bee_heat_param_df <- rbind(brood_bee_param,worker_bee_param) %>% rbind(.,queen_bee_param) %>% rbind(.,all_bee_param) %>% rbind(.,none_bee_param)
ggplot(bee_heat_param_df, aes(x = type, y = pBroodHeat))+
geom_boxplot()+
theme_classic()

heat_glm <- glm(pBroodHeat ~ 0 + type, data = bee_heat_param_df)
contrast(emmeans(heat_glm, "type"), "pairwise", adjust = "Tukey")
contrast estimate SE df t.ratio p.value
All - Brood -0.0545 0.00819 3835 -6.650 <.0001
All - None -0.4391 0.00819 3835 -53.600 <.0001
All - Queen -0.2398 0.00819 3835 -29.271 <.0001
All - Worker -0.3607 0.00819 3835 -44.021 <.0001
Brood - None -0.3847 0.00772 3835 -49.798 <.0001
Brood - Queen -0.1853 0.00772 3835 -23.993 <.0001
Brood - Worker -0.3062 0.00772 3835 -39.638 <.0001
None - Queen 0.1993 0.00772 3835 25.805 <.0001
None - Worker 0.0785 0.00772 3835 10.160 <.0001
Queen - Worker -0.1208 0.00772 3835 -15.645 <.0001
P value adjustment: tukey method for comparing a family of 5 estimates
ggplot(all_bee_param %>% na.omit(), aes(x = qha, y = bhd, color = pBroodHeat))+
geom_point()+
scale_color_viridis()+
theme_classic()

rounded_all_bee_param <- all_bee_param %>% mutate(rounded_qha = round_any(qha, 0.25),
rounded_bhd = round_any(bhd, 0.25))
ggplot(rounded_all_bee_param %>% na.omit(), aes(x = rounded_qha, y = rounded_bhd, z = pBroodHeat))+
geom_contour_filled()+
theme_classic()

NA
NA
Looking at correlation between heat parameter and brood in the
place
ggplot(bee_heat_param_df %>% filter(bhd > 0), aes(x = bhd, y = pBroodHeat))+
geom_point()+
geom_smooth(method = 'glm', formula = 'y ~ x')+
theme_classic()

cor.test((bee_heat_param_df %>% filter(bhd > 0))$bhd,(bee_heat_param_df %>% filter(bhd > 0))$pBroodHeat)
Pearson's product-moment correlation
data: (bee_heat_param_df %>% filter(bhd > 0))$bhd and (bee_heat_param_df %>% filter(bhd > 0))$pBroodHeat
t = -32.341, df = 1438, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.6778413 -0.6179628
sample estimates:
cor
-0.6489056
ggplot(bee_heat_param_df %>% filter(qha > 0), aes(x = qha, y = pBroodHeat))+
geom_point()+
geom_smooth(method = 'glm', formula = 'y ~ x')+
theme_classic()

cor.test((bee_heat_param_df %>% filter(qha > 0))$qha,(bee_heat_param_df %>% filter(qha > 0))$pBroodHeat)
Pearson's product-moment correlation
data: (bee_heat_param_df %>% filter(qha > 0))$qha and (bee_heat_param_df %>% filter(qha > 0))$pBroodHeat
t = -27.375, df = 1438, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.6182844 -0.5503031
sample estimates:
cor
-0.5853215
ggplot(bee_heat_param_df %>% filter(wha > 0), aes(x = wha, y = pBroodHeat))+
geom_point()+
geom_smooth(method = 'glm', formula = 'y ~ x')+
theme_classic()

cor.test((bee_heat_param_df %>% filter(wha > 0))$wha,(bee_heat_param_df %>% filter(wha > 0))$pBroodHeat)
Pearson's product-moment correlation
data: (bee_heat_param_df %>% filter(wha > 0))$wha and (bee_heat_param_df %>% filter(wha > 0))$pBroodHeat
t = -5.9184, df = 1438, p-value = 4.058e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.2042354 -0.1033708
sample estimates:
cor
-0.1542049
Let’s make some stepwise models! Just with the all data for now so
there aren’t so many zeroes
hive_pca <- prcomp(bee_heat_param_df %>% select(-c(...1, trial_n, type, days, n,
pBrood, pPollen, pHoney, pEmpty,
pBroodHeat, pPollenHeat, pHoneyHeat, pEmptyHeat,
broodMetric,pollenRing)),
center = TRUE,scale. = TRUE)
Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric
heat_brood_glm <- glm(pBroodHeat ~ ., data = all_bee_param %>% dplyr::select(-c(...1, trial_n, type, days, n,
pBrood, pPollen, pHoney, pEmpty,
pPollenHeat, pHoneyHeat, pEmptyHeat,
broodMetric,pollenRing)))
stepped_model <- stepAIC(heat_brood_glm, direction = "both")
Start: AIC=-1644.08
pBroodHeat ~ rb + rn + w + pph + ph + pp + k + bhd + wha + qha
Df Deviance AIC
- wha 1 2.7657 -1646.0
- rb 1 2.7660 -1646.0
- k 1 2.7710 -1644.8
- pph 1 2.7733 -1644.3
<none> 2.7655 -1644.1
- rn 1 2.7757 -1643.7
- w 1 2.7808 -1642.5
- ph 1 2.7828 -1642.1
- pp 1 2.8025 -1637.6
- qha 1 3.6132 -1475.0
- bhd 1 4.1686 -1383.5
Step: AIC=-1646.02
pBroodHeat ~ rb + rn + w + pph + ph + pp + k + bhd + qha
Df Deviance AIC
- rb 1 2.7662 -1647.9
- k 1 2.7711 -1646.8
- pph 1 2.7739 -1646.2
<none> 2.7657 -1646.0
- rn 1 2.7759 -1645.7
- w 1 2.7812 -1644.5
+ wha 1 2.7655 -1644.1
- ph 1 2.7832 -1644.0
- pp 1 2.8028 -1639.5
- qha 1 3.6153 -1476.6
- bhd 1 4.1692 -1385.3
Step: AIC=-1647.9
pBroodHeat ~ rn + w + pph + ph + pp + k + bhd + qha
Df Deviance AIC
- k 1 2.7714 -1648.7
- pph 1 2.7743 -1648.0
<none> 2.7662 -1647.9
- rn 1 2.7764 -1647.5
- w 1 2.7821 -1646.2
+ rb 1 2.7657 -1646.0
- ph 1 2.7833 -1646.0
+ wha 1 2.7660 -1646.0
- pp 1 2.8029 -1641.5
- qha 1 3.6153 -1478.6
- bhd 1 4.1692 -1387.3
Step: AIC=-1648.71
pBroodHeat ~ rn + w + pph + ph + pp + bhd + qha
Df Deviance AIC
<none> 2.7714 -1648.7
- pph 1 2.7802 -1648.7
- rn 1 2.7807 -1648.6
+ k 1 2.7662 -1647.9
- ph 1 2.7879 -1646.9
+ rb 1 2.7711 -1646.8
+ wha 1 2.7713 -1646.7
- w 1 2.7888 -1646.7
- pp 1 2.8093 -1642.0
- qha 1 3.6258 -1478.7
- bhd 1 4.1708 -1389.1
Anova(stepped_model)
Analysis of Deviance Table (Type II tests)
Response: pBroodHeat
LR Chisq Df Pr(>Chisq)
rn 2.11 1 0.146522
w 3.96 1 0.046605 *
pph 2.00 1 0.157613
ph 3.77 1 0.052131 .
pp 8.63 1 0.003303 **
bhd 319.12 1 < 2.2e-16 ***
qha 194.83 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ggplot(all_bee_param, aes(bhd, pBroodHeat, color = pBroodHeat))+
geom_point()+
scale_color_viridis()+
theme_classic()

ggplot(all_bee_param, aes(qha, pBroodHeat, color = pBroodHeat))+
geom_point()+
scale_color_viridis()+
theme_classic()

now let’s see about the order of those models
bhd_m <- glm(pBroodHeat ~ bhd, data = all_bee_param)
bhd_2_m <- glm(pBroodHeat ~ poly(bhd,2), data = all_bee_param)
bhd_3_m <- glm(pBroodHeat ~ poly(bhd,3), data = all_bee_param)
bhd_ln_m <- glm(pBroodHeat ~ log(bhd), data = all_bee_param)
ICtab(bhd_m,bhd_2_m,bhd_3_m,bhd_ln_m)
dAIC df
bhd_2_m 0.0 4
bhd_3_m 1.3 5
bhd_ln_m 15.3 3
bhd_m 43.6 3
ggplot(all_bee_param, aes(bhd, pBroodHeat, color = pBroodHeat))+
geom_point()+
geom_smooth(method = "glm", formula = "y ~ poly(x,2)", se = FALSE) +
scale_color_viridis()+
theme_classic()

qha_m <- glm(pBroodHeat ~ qha, data = all_bee_param)
qha_2_m <- glm(pBroodHeat ~ poly(qha,2), data = all_bee_param)
qha_3_m <- glm(pBroodHeat ~ poly(qha,3), data = all_bee_param)
qha_ln_m <- glm(pBroodHeat ~ log(qha), data = all_bee_param)
ICtab(qha_m,qha_2_m,qha_3_m,qha_ln_m)
dAIC df
qha_m 0.0 3
qha_3_m 0.3 5
qha_2_m 1.9 4
qha_ln_m 31.9 3
ggplot(all_bee_param, aes(qha, pBroodHeat, color = pBroodHeat))+
geom_point()+
geom_smooth(method = "glm", formula = "y ~ x", se = FALSE) +
scale_color_viridis()+
theme_classic()

From the none data, looking at the good ones. The pollen ring metric
seems a bit off
Step AIC to see what predicts good or bad
Anova(stepped_good_model)
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Analysis of Deviance Table (Type II tests)
Response: good_or_bad
LR Chisq Df Pr(>Chisq)
rn 22.3956 1 2.219e-06 ***
k 7.7241 1 0.005449 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ggplot(none_bee_param_good_bad, aes(x = n, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = rb, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = rn, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = w, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = pph, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = ph, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = pp, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

ggplot(none_bee_param_good_bad, aes(x = k, fill = good_or_bad))+
geom_histogram(bins = 10)+
theme_classic()

OLDER CODE, looking at model type, we decided on M2 ####
rand_bee_param <- read.csv("bee_data_isaac_rand.csv") %>% mutate(type = "Rand")
m1_bee_param <- read.csv("bee_data_isaac_m1.csv") %>% mutate(type = "M1") %>% select(-trial_n)
m2_bee_param <- read.csv("bee_data_isaac_m2.csv") %>% mutate(type = "M2") %>% select(-trial_n)
m2E_bee_param <- read.csv("bee_data_isaac_m2_empty.csv") %>% mutate(type = "M2E") %>% select(-trial_n)
m2K_bee_param <- read.csv("bee_data_isaac_m2_katie.csv") %>% mutate(type = "M2K") %>% select(-trial_n)
bee_param_df <- rbind(m1_bee_param,m2_bee_param) %>% rbind(.,rand_bee_param) %>% rbind(.,m2E_bee_param) %>% rbind(.,m2K_bee_param)
bee_param_df_perc <- bee_param_df %>% select(c(type,pBrood,pHoney,pPollen,pEmpty)) %>%
pivot_longer(c(pBrood,pHoney,pPollen,pEmpty))
ggplot(bee_param_df, aes(x = broodMetric, y = pollenRing, color = type))+
geom_point(alpha = 0.25)+
ylim(0,16)+
xlim(0,6)+
theme_classic()
ggplot(bee_param_df_perc, aes(x = type, y = value, fill = name))+
geom_boxplot()+
theme_classic()
ggplot(m1_bee_param, aes(x = pPollen, y = pph))+
geom_point()+
theme_classic()
broodMetricGLM_m0 <- glm(broodMetric ~ 1, data = m2_bee_param)
broodMetricGLM_m1 <- glm(broodMetric ~ n + rb + rn + w + pph + ph + pp + k, data = m2_bee_param)
broodMetricGLM_step <- step(broodMetricGLM_m0, direction = "both", scope = formula(broodMetricGLM_m1), trace = 0)
summary(broodMetricGLM_step)
pollenRingGLM_m0 <- glm(pollenRing ~ 1, data = m2_bee_param)
pollenRingGLM_m1 <- glm(pollenRing ~ n + rb + rn + w + pph + ph + pp + k, data = m2_bee_param)
pollenRingGLM_step <- step(pollenRingGLM_m0, direction = "both", scope = formula(pollenRingGLM_m1), trace = 0)
summary(pollenRingGLM_step)
queen_pos_df <- read.csv("queen_pos_data_28_5.csv")
queen_pos_df_heatmap <- queen_pos_df %>% mutate(QueenXRound = round_any(QueenX,5),
QueenYRound = round_any(QueenY,5)) %>%
group_by(QueenXRound,QueenYRound) %>%
summarise(meanAngleMoved = mean(AngleMoved),
count = n())
`summarise()` has grouped output by 'QueenXRound'. You can override using the `.groups` argument.
ggplot(queen_pos_df, aes(x = QueenX, y = QueenY))+
geom_path()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = Dist2Heat))+
geom_point()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = cos(AngleOppHeat)))+
geom_point()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = sin(AngleOppHeat)))+
geom_point()+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = cos(meanAngleMoved)))+
geom_point(size = 5)+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = sin(meanAngleMoved)))+
geom_point(size = 5)+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = count))+
geom_point(size = 5)+
geom_point(aes(x=11,y=48.5),colour="red") +
scale_color_viridis() +
theme_classic()

---
title: "Hive Parameter Analysis"
output: html_notebook
---

```{r Load Packages}
library(MASS)
library(tidyverse)
library(ggplot2)
library(readr)
library(viridis)
library(ggfortify)
library(bbmle) #For ICtab
library(car)
library(emmeans)

 
round_any <- function(x, accuracy, f=round){f(x/ accuracy) * accuracy}

```

```{r}

round_any <- function(x, accuracy, f=round){f(x/ accuracy) * accuracy}

```


```{r Read in Data}
# trial_types <- c("none","brood","worker","queen","all")
# 
# for (t in trial_types){
#   bee_param_df <- list.files(path=paste0("hive_data/heat_",t,"/"), full.names = TRUE) %>%
#                 lapply(read_csv, show_col_types = FALSE) %>%
#                 bind_rows %>%
#                 mutate(filenames = list.files(path=paste0("hive_data/heat_",t,"/"), full.names = TRUE))
# 
#   write.csv(bee_param_df,paste0("bee_data_isaac_heat_",t,".csv"),row.names = FALSE)
# }

```

What these all mean:
n = queen cells per hour
rb = brood radius
rn = necter radius
w = total daily honey
pph = pollen ratio
ph = honey consumption ratio
pp = pollen consumption ratio
k = consumption probability value

broodMetric = average number of brood surrounding other brood
pollenRing = average min distance between honey and brood

```{r}
brood_bee_param <- read.csv("bee_data_isaac_heat_brood.csv") %>% mutate(type = "Brood") %>% mutate(wha = 0, qha = 0)
worker_bee_param <- read.csv("bee_data_isaac_heat_worker.csv") %>% mutate(type = "Worker") %>% mutate(bhd = 0, qha = 0)
queen_bee_param <- read.csv("bee_data_isaac_heat_queen.csv") %>% mutate(type = "Queen") %>% mutate(bhd = 0, wha = 0)
none_bee_param <- read.csv("bee_data_isaac_heat_none.csv") %>% mutate(type = "None") %>% mutate(bhd = 0,wha = 0, qha = 0)
all_bee_param <- read.csv("bee_data_isaac_heat_all.csv") %>% mutate(type = "All")

bee_heat_param_df <- rbind(brood_bee_param,worker_bee_param) %>% rbind(.,queen_bee_param) %>% rbind(.,all_bee_param) 
```


```{r}
ggplot(bee_heat_param_df, aes(x = type, y = pBroodHeat))+
  geom_boxplot()+
  theme_classic()

heat_glm <- glm(pBroodHeat ~ 0 + type, data = bee_heat_param_df)

contrast(emmeans(heat_glm, "type"), "pairwise", adjust = "Tukey")

```


```{r}

ggplot(all_bee_param %>% na.omit(), aes(x = qha, y = bhd, color = pBroodHeat))+
  geom_point()+
  scale_color_viridis()+
  theme_classic()

rounded_all_bee_param <- all_bee_param %>% mutate(rounded_qha = round_any(qha, 0.25),
                                                 rounded_bhd = round_any(bhd, 0.25))

ggplot(rounded_all_bee_param %>% na.omit(), aes(x = rounded_qha, y = rounded_bhd, z = pBroodHeat))+
  geom_contour_filled()+
  theme_classic()


```




```{r}

ggplot(bee_heat_param_df %>% na.omit(), aes(x = broodMetric, y = pollenRing, color = pHoney))+
  geom_point(alpha = 0.1)+
  geom_rect(aes(xmin = 5, xmax = 6, ymin = 12, ymax = 30), fill = NA)+
  scale_color_viridis()+
  theme_classic()

max(bee_heat_param_df$pollenRing)

bee_heat_param_df %>% filter(broodMetric >= 5 & pollenRing >= 10)
```



Looking at correlation between heat parameter and brood in the place

```{r}
ggplot(bee_heat_param_df %>% filter(bhd > 0), aes(x = bhd, y = pBroodHeat))+
  geom_point()+
  geom_smooth(method = 'glm', formula = 'y ~ x')+
  theme_classic()

cor.test((bee_heat_param_df %>% filter(bhd > 0))$bhd,(bee_heat_param_df %>% filter(bhd > 0))$pBroodHeat)

ggplot(bee_heat_param_df %>% filter(qha > 0), aes(x = qha, y = pBroodHeat))+
  geom_point()+
  geom_smooth(method = 'glm', formula = 'y ~ x')+
  theme_classic()

cor.test((bee_heat_param_df %>% filter(qha > 0))$qha,(bee_heat_param_df %>% filter(qha > 0))$pBroodHeat)

ggplot(bee_heat_param_df %>% filter(wha > 0), aes(x = wha, y = pBroodHeat))+
  geom_point()+
  geom_smooth(method = 'glm', formula = 'y ~ x')+
  theme_classic()

cor.test((bee_heat_param_df %>% filter(wha > 0))$wha,(bee_heat_param_df %>% filter(wha > 0))$pBroodHeat)
```

Let's make some stepwise models! Just with the all data for now so there aren't so many zeroes

```{r}

hive_pca <- prcomp(bee_heat_param_df %>% select(-c(...1, trial_n, type, days, n,
                                                   pBrood, pPollen, pHoney, pEmpty,
                                                   pBroodHeat, pPollenHeat, pHoneyHeat, pEmptyHeat,
                                                   broodMetric,pollenRing)),
                   center = TRUE,scale. = TRUE)
summary(hive_pca)
hive_pca$rotation[,1:2]

autoplot(hive_pca, colour = "pBroodHeat", loadings = TRUE, loadings.label = TRUE,
         data = bee_heat_param_df)+
  scale_color_viridis()+
  theme_classic()

ggplot(bee_heat_param_df, aes(pPollen, pBroodHeat))+
  geom_point()+
  theme_classic()

ggplot(bee_heat_param_df, aes(wha, pPollen))+
  geom_point()+
  theme_classic()

ggplot(bee_heat_param_df, aes(pph, pp, color = pPollen))+
  geom_point()+
  scale_color_viridis()+
  theme_classic()
```

```{r}
heat_brood_glm <- glm(pBroodHeat ~ ., data = all_bee_param %>% dplyr::select(-c(...1, trial_n, type, days, n,
                                                   pBrood, pPollen, pHoney, pEmpty,
                                                   pPollenHeat, pHoneyHeat, pEmptyHeat,
                                                   broodMetric,pollenRing)))

stepped_model <- stepAIC(heat_brood_glm, direction = "both")


Anova(stepped_model)

ggplot(all_bee_param, aes(bhd, pBroodHeat, color = pBroodHeat))+
  geom_point()+
  scale_color_viridis()+
  theme_classic()

ggplot(all_bee_param, aes(qha, pBroodHeat, color = pBroodHeat))+
  geom_point()+
  scale_color_viridis()+
  theme_classic()
```

now let's see about the order of those models

```{r}

bhd_m <- glm(pBroodHeat ~ bhd, data = all_bee_param)
bhd_2_m <- glm(pBroodHeat ~ poly(bhd,2), data = all_bee_param)
bhd_3_m <- glm(pBroodHeat ~ poly(bhd,3), data = all_bee_param)
bhd_ln_m <- glm(pBroodHeat ~ log(bhd), data = all_bee_param)

ICtab(bhd_m,bhd_2_m,bhd_3_m,bhd_ln_m)

ggplot(all_bee_param, aes(bhd, pBroodHeat, color = pBroodHeat))+
  geom_point()+
  geom_smooth(method = "glm", formula = "y ~ poly(x,2)", se = FALSE) +
  scale_color_viridis()+
  theme_classic()


qha_m <- glm(pBroodHeat ~ qha, data = all_bee_param)
qha_2_m <- glm(pBroodHeat ~ poly(qha,2), data = all_bee_param)
qha_3_m <- glm(pBroodHeat ~ poly(qha,3), data = all_bee_param)
qha_ln_m <- glm(pBroodHeat ~ log(qha), data = all_bee_param)

ICtab(qha_m,qha_2_m,qha_3_m,qha_ln_m)

ggplot(all_bee_param, aes(qha, pBroodHeat, color = pBroodHeat))+
  geom_point()+
  geom_smooth(method = "glm", formula = "y ~ x", se = FALSE) +
  scale_color_viridis()+
  theme_classic()

```

From the none data, looking at the good ones. The pollen ring metric seems a bit off
```{r}

good_hives <- c("hiveplot_00720_D030_T00111_none.pdf","hiveplot_00720_D030_T00125_none.pdf","hiveplot_00720_D030_T00211_none.pdf",
                "hiveplot_00720_D030_T00219_none.pdf","hiveplot_00720_D030_T00333_none.pdf","hiveplot_00720_D030_T00530_none.pdf",
                "hiveplot_00720_D030_T00619_none.pdf","hiveplot_00720_D030_T00706_none.pdf","hiveplot_00720_D030_T00923_none.pdf",
                "hiveplot_00720_D030_T00928_none.pdf","hiveplot_00720_D030_T01430_none.pdf","hiveplot_00720_D030_T01430_none.pdf",
                "hiveplot_00720_D030_T01720_none.pdf","hiveplot_00720_D030_T01914_none.pdf","hiveplot_00720_D030_T01915_none.pdf",
                "hiveplot_00720_D030_T01938_none.pdf","hiveplot_00720_D030_T02001_none.pdf","hiveplot_00720_D030_T02028_none.pdf")

good_csvs <- c()

for(hive in good_hives){
  number <- substring(hive,22,26)
  
  file <- paste0("hive_data/heat_none//Beehive Data Out Trial ",number,"_none.csv")
  
  good_csvs <- c(file,good_csvs)
}

bad_hives <- c("hiveplot_00720_D030_T00108_none.pdf","hiveplot_00720_D030_T00122_none.pdf","hiveplot_00720_D030_T00209_none.pdf",
               "hiveplot_00720_D030_T00218_none.pdf","hiveplot_00720_D030_T00236_none.pdf","hiveplot_00720_D030_T00328_none.pdf",
               "hiveplot_00720_D030_T00335_none.pdf","hiveplot_00720_D030_T00421_none.pdf","hiveplot_00720_D030_T00614_none.pdf",
               "hiveplot_00720_D030_T00635_none.pdf","hiveplot_00720_D030_T00711_none.pdf","hiveplot_00720_D030_T00935_none.pdf",
               "hiveplot_00720_D030_T01137_none.pdf","hiveplot_00720_D030_T01313_none.pdf","hiveplot_00720_D030_T01525_none.pdf",
               "hiveplot_00720_D030_T01623_none.pdf","hiveplot_00720_D030_T01725_none.pdf","hiveplot_00720_D030_T01928_none.pdf")

bad_csvs <- c()

for(hive in bad_hives){
  number <- substring(hive,22,26)
  
  file <- paste0("hive_data/heat_none//Beehive Data Out Trial ",number,"_none.csv")
  
  bad_csvs <- c(file,bad_csvs)
}
                                                                  
none_bee_param_good_bad <- none_bee_param %>% filter(filenames %in% good_csvs | filenames %in% bad_csvs) %>%
                                          mutate(bool_good_or_bad = ifelse(filenames %in% good_csvs,1,0),
                                                 good_or_bad = ifelse(filenames %in% good_csvs,"Good","Bad"))
none_bee_param_good_bad
```
Step AIC to see what predicts good or bad

```{r}

good_hive_glm <- glm(bool_good_or_bad ~ ., data = none_bee_param_good_bad %>% dplyr::select(-c(...1, trial_n, type, days, n,
                                                   pBrood, pPollen, pHoney, pEmpty,
                                                   pBroodHeat, pPollenHeat, pHoneyHeat, pEmptyHeat,
                                                   broodMetric,pollenRing)),
                     family = "binomial")

stepped_good_model <- stepAIC(good_hive_glm, direction = "both")


Anova(stepped_good_model)


```

```{r}
ggplot(none_bee_param_good_bad, aes(x = n, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = rb, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = rn, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = w, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = pph, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = ph, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = pp, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()

ggplot(none_bee_param_good_bad, aes(x = k, fill = good_or_bad))+
  geom_histogram(bins = 10)+
  theme_classic()
```

####
OLDER CODE, looking at model type, we decided on M2
####


```{r}
rand_bee_param <- read.csv("bee_data_isaac_rand.csv") %>% mutate(type = "Rand")
m1_bee_param <- read.csv("bee_data_isaac_m1.csv") %>% mutate(type = "M1") %>% select(-trial_n)
m2_bee_param <- read.csv("bee_data_isaac_m2.csv") %>% mutate(type = "M2") %>% select(-trial_n)
m2E_bee_param <- read.csv("bee_data_isaac_m2_empty.csv") %>% mutate(type = "M2E") %>% select(-trial_n)
m2K_bee_param <- read.csv("bee_data_isaac_m2_katie.csv") %>% mutate(type = "M2K") %>% select(-trial_n)

bee_param_df <- rbind(m1_bee_param,m2_bee_param) %>% rbind(.,rand_bee_param) %>% rbind(.,m2E_bee_param) %>% rbind(.,m2K_bee_param)
```


```{r}
bee_param_df_perc <- bee_param_df %>% select(c(type,pBrood,pHoney,pPollen,pEmpty)) %>%
                                      pivot_longer(c(pBrood,pHoney,pPollen,pEmpty))
```


```{r Overall Brood and Pollen}

ggplot(bee_param_df, aes(x = broodMetric, y = pollenRing, color = type))+
  geom_point(alpha = 0.25)+
  ylim(0,16)+
  xlim(0,6)+
  theme_classic()

ggplot(bee_param_df_perc, aes(x = type, y = value, fill = name))+
  geom_boxplot()+
  theme_classic()

ggplot(m1_bee_param, aes(x = pPollen, y = pph))+
  geom_point()+
  theme_classic()

```

```{r Brood Metric GLMs}
broodMetricGLM_m0 <- glm(broodMetric ~ 1, data = m2_bee_param)
broodMetricGLM_m1 <- glm(broodMetric ~ n + rb + rn + w + pph + ph + pp + k, data = m2_bee_param)
broodMetricGLM_step <- step(broodMetricGLM_m0, direction = "both", scope = formula(broodMetricGLM_m1), trace = 0)

summary(broodMetricGLM_step)
```

```{r Pollen Ring GLMs}
pollenRingGLM_m0 <- glm(pollenRing ~ 1, data = m2_bee_param)
pollenRingGLM_m1 <- glm(pollenRing ~ n + rb + rn + w + pph + ph + pp + k, data = m2_bee_param)
pollenRingGLM_step <- step(pollenRingGLM_m0, direction = "both", scope = formula(pollenRingGLM_m1), trace = 0)

summary(pollenRingGLM_step)
```

```{r}
queen_pos_df <- read.csv("queen_pos_data_28_5.csv")

queen_pos_df_heatmap <- queen_pos_df %>% mutate(QueenXRound = round_any(QueenX,5),
                                                QueenYRound = round_any(QueenY,5)) %>%
                                        group_by(QueenXRound,QueenYRound) %>%
                                        summarise(meanAngleMoved = mean(AngleMoved),
                                                  count = n())

```


```{r}
ggplot(queen_pos_df, aes(x = QueenX, y = QueenY))+
  geom_path()+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()

ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = Dist2Heat))+
  geom_point()+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()

ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = cos(AngleOppHeat)))+
  geom_point()+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()

ggplot(queen_pos_df, aes(x = QueenX, y = QueenY, color = sin(AngleOppHeat)))+
  geom_point()+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()

ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = cos(meanAngleMoved)))+
  geom_point(size = 5)+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()

ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = sin(meanAngleMoved)))+
  geom_point(size = 5)+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()

ggplot(queen_pos_df_heatmap, aes(x = QueenXRound, y = QueenYRound, color = count))+
  geom_point(size = 5)+
  geom_point(aes(x=11,y=48.5),colour="red") +
  scale_color_viridis() +
  theme_classic()
```






